# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18+ | 6.9 | 94% | NaN | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | English | Set seven years after the world has become a f... | 60.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
| 1 | 2 | Philadelphia | 1993 | 13+ | 8.8 | 80% | NaN | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | English | The gang, 5 raging alcoholic, narcissists run ... | 22.0 | tv series | 18.0 | 1 | 0 | 0 | 0 | 1 |
| 2 | 3 | Roma | 2018 | 18+ | 8.7 | 93% | NaN | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | English | In this British historical drama, the turbulen... | 52.0 | tv series | 2.0 | 1 | 0 | 0 | 0 | 1 |
| 3 | 4 | Amy | 2015 | 18+ | 7.0 | 87% | NaN | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | English | A family drama focused on three generations of... | 60.0 | tv series | 6.0 | 1 | 0 | 1 | 1 | 1 |
| 4 | 5 | The Young Offenders | 2016 | NaN | 8.0 | 100% | NaN | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | English | NaN | 30.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
# profile = ProfileReport(df_tvshows)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 1954
IMDb 556
Rotten Tomatoes 4194
Directors 5158
Cast 486
Genres 323
Country 549
Language 638
Plotline 2493
Runtime 1410
Seasons 679
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 35.972018
IMDb 10.235641
Rotten Tomatoes 77.209131
Directors 94.955817
Cast 8.946981
Genres 5.946244
Country 10.106775
Language 11.745214
Plotline 45.894698
Runtime 25.957290
Kind 0.000000
Seasons 12.500000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 21
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Seasons object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Seasons 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | Set seven years after the world has become a f... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | ... | The gang, 5 raging alcoholic, narcissists run ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | In this British historical drama, the turbulen... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 3 | 4 | Amy | 2015 | 18 | 7 | 87 | NA | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | ... | A family drama focused on three generations of... | 60 | tv series | 6 | 1 | 0 | 1 | 1 | 1 | Netflix |
| 4 | 5 | The Young Offenders | 2016 | NR | 8 | 100 | NA | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | ... | NA | 30 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
df_tvshows.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.0 |
| mean | 2716.500000 | 2010.668446 | 0.341311 | 0.293999 | 0.403351 | 0.033689 | 1.0 |
| std | 1568.227662 | 11.726176 | 0.474193 | 0.455633 | 0.490615 | 0.180445 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 25% | 1358.750000 | 2009.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 50% | 2716.500000 | 2014.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 75% | 4074.250000 | 2017.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.0 |
| max | 5432.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 |
df_tvshows.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.031346 | -0.646330 | 0.034293 | 0.441264 | 0.195409 | NaN |
| Year | -0.031346 | 1.000000 | 0.222316 | -0.065807 | -0.198675 | -0.022741 | NaN |
| Netflix | -0.646330 | 0.222316 | 1.000000 | -0.366515 | -0.515086 | -0.119344 | NaN |
| Hulu | 0.034293 | -0.065807 | -0.366515 | 1.000000 | -0.377374 | -0.075701 | NaN |
| Prime Video | 0.441264 | -0.198675 | -0.515086 | -0.377374 | 1.000000 | -0.151442 | NaN |
| Disney+ | 0.195409 | -0.022741 | -0.119344 | -0.075701 | -0.151442 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)
# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
# udf_tvshows
# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]
df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]
df_tvshows_genres = df_tvshows.copy()
df_tvshows_genres.drop(df_tvshows_genres.loc[df_tvshows_genres['Genres'] == "NA"].index, inplace = True)
# df_tvshows_genres = df_tvshows_genres[df_tvshows_genres.Genre != "NA"]
# df_tvshows_genres['Genres'] = df_tvshows_genres['Genres'].astype(str)
df_tvshows_count_genres = df_tvshows_genres.copy()
df_tvshows_genre = df_tvshows_genres.copy()
df_tvshows_genre_all = df_tvshows_genres.copy()
# Create genres dict where key=name and value = number of genres
genres = {}
for i in df_tvshows_count_genres['Genres'].dropna():
if i != "NA":
#print(i,len(i.split(',')))
genres[i] = len(i.split(','))
else:
genres[i] = 0
# Add this information to our dataframe as a new column
df_tvshows_count_genres['Number of Genres'] = df_tvshows_count_genres['Genres'].map(genres).astype(int)
df_tvshows_mixed_genres = df_tvshows_count_genres.copy()
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Netflix'] == 1]
hulu_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Hulu'] == 1]
prime_video_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Prime Video'] == 1]
disney_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Disney+'] == 1]
plt.figure(figsize = (10, 10))
corr = df_tvshows_count_genres.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_genres_most_tvshows = df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = False).reset_index()
df_genres_most_tvshows = df_genres_most_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_genres['Number of Genres'] == (df_tvshows_count_genres['Number of Genres'].max()))
# df_genres_most_tvshows = df_tvshows_count_genres[filter]
# mostest_rated_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Number of Genres'].idxmax()]
print('\nTV Shows with Highest Ever Number of Genres are : \n')
df_genres_most_tvshows.head(5)
TV Shows with Highest Ever Number of Genres are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2406 | Steven Universe | 2013 | 7 | 8.2 | 100 | NA | Zach Callison,Deedee Magno,Michaela Dietz,Este... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,South Korea,Spain,Japan,Mexico | ... | 11 | tv series | 6 | 0 | 1 | 0 | 0 | 1 | Hulu | 11 |
| 1 | 5314 | Gargoyles | 1994 | 7 | 8.1 | NA | NA | Keith David,Salli Richardson-Whitfield,Jeff Be... | Animation,Action,Adventure,Crime,Drama,Family,... | United States | ... | 30 | tv series | 2 | 0 | 0 | 0 | 1 | 1 | Disney+ | 10 |
| 2 | 2275 | Gravity Falls | 2012 | 7 | 8.9 | 100 | NA | Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ... | Animation,Adventure,Comedy,Drama,Family,Fantas... | United States,Argentina,Australia,United Kingd... | ... | 23 | tv series | 2 | 0 | 1 | 0 | 1 | 1 | Disney+ | 10 |
| 3 | 3896 | Infinity Train | 2019 | 7 | 8.5 | 100 | NA | Ashley Johnson,Owen Dennis,Jeremy Crutchley,Ki... | Animation,Action,Adventure,Drama,Family,Fantas... | United States | ... | 11 | tv series | 4 | 0 | 0 | 1 | 0 | 1 | Prime Video | 10 |
| 4 | 2096 | Spy Kids: Mission Critical | 2018 | 7 | 4.7 | NA | NA | Nicholas Coombe,Ashley Bornancin,Carter Hastin... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,Canada | ... | NA | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | 9 |
5 rows × 22 columns
fig = px.bar(y = df_genres_most_tvshows['Title'][:15],
x = df_genres_most_tvshows['Number of Genres'][:15],
color = df_genres_most_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Highest Number of Genres : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_genres_least_tvshows = df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = True).reset_index()
df_genres_least_tvshows = df_genres_least_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_genres['Number of Genres'] == (df_tvshows_count_genres['Number of Genres'].min()))
# df_genres_least_tvshows = df_tvshows_count_genres[filter]
print('\nTV Shows with Lowest Ever Number of Genres are : \n')
df_genres_least_tvshows.head(5)
TV Shows with Lowest Ever Number of Genres are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5431 | Love & Vets | 2017 | 13 | 8.6 | NA | NA | Will Draper,Francoise Tyler,Olivia,Carrie McCo... | Reality-TV | United States | ... | 42 | tv series | 1 | 0 | 0 | 0 | 1 | 1 | Disney+ | 1 |
| 1 | 1528 | Louis Theroux: Miami Mega-Jail | 2011 | 18 | 7.6 | NA | NA | Louis Theroux | Documentary | United Kingdom | ... | 120 | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix | 1 |
| 2 | 3466 | Tengo Talento, Mucho Talento | 2012 | NR | 7.2 | NA | NA | Mary Fredette,Denisse Padilla,Elena Diaz,Cesar... | Reality-TV | United States | ... | NA | tv series | 18 | 0 | 1 | 0 | 0 | 1 | Hulu | 1 |
| 3 | 3468 | Vets Saving Pets | 2018 | NR | 7.4 | NA | NA | Ed Nash | Documentary | United States | ... | NA | tv series | 2 | 0 | 1 | 0 | 0 | 1 | Hulu | 1 |
| 4 | 3469 | The View | 1997 | 16 | 2.6 | NA | NA | Whoopi Goldberg,Joy Behar,Virginia Hamilton,Su... | Talk-Show | United States | ... | 60 | tv series | NA | 0 | 1 | 0 | 0 | 1 | Hulu | 1 |
5 rows × 22 columns
fig = px.bar(y = df_genres_least_tvshows['Title'][:15],
x = df_genres_least_tvshows['Number of Genres'][:15],
color = df_genres_least_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Lowest Number of Genres : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_tvshows_count_genres['Number of Genres'].unique().shape[0]}' unique Number of Genres s were Given, They were Like this,\n
{df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = False)['Number of Genres'].unique()}\n
The Highest Number of Genres Ever Any TV Show Got is '{df_genres_most_tvshows['Title'][0]}' : '{df_genres_most_tvshows['Number of Genres'].max()}'\n
The Lowest Number of Genres Ever Any TV Show Got is '{df_genres_least_tvshows['Title'][0]}' : '{df_genres_least_tvshows['Number of Genres'].min()}'\n
''')
Total '11' unique Number of Genres s were Given, They were Like this,
[11 10 9 8 7 6 5 4 3 2 1]
The Highest Number of Genres Ever Any TV Show Got is 'Steven Universe' : '11'
The Lowest Number of Genres Ever Any TV Show Got is 'Love & Vets' : '1'
netflix_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Netflix']==1].reset_index()
netflix_genres_most_tvshows = netflix_genres_most_tvshows.drop(['index'], axis = 1)
netflix_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Netflix']==1].reset_index()
netflix_genres_least_tvshows = netflix_genres_least_tvshows.drop(['index'], axis = 1)
netflix_genres_most_tvshows.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2096 | Spy Kids: Mission Critical | 2018 | 7 | 4.7 | NA | NA | Nicholas Coombe,Ashley Bornancin,Carter Hastin... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,Canada | ... | NA | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | 9 |
| 1 | 723 | Case Closed | 1996 | 7 | 8.4 | NA | NA | Minami Takayama,Wakana Yamazaki,Hoang Khuyet,A... | Animation,Action,Adventure,Comedy,Crime,Drama,... | Japan,Italy,United Kingdom,Mexico,Spain | ... | 25 | tv series | 53 | 1 | 1 | 0 | 0 | 1 | Netflix | 9 |
| 2 | 810 | Daybreak | 2019 | 18 | 6.7 | 70 | NA | Colin Ford,Alyvia Alyn Lind,Sophie Simnett,Aus... | Action,Adventure,Comedy,Drama,Fantasy,Horror,M... | United States | ... | 60 | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix | 9 |
| 3 | 833 | Scooby-Doo! Mystery Incorporated | 2010 | 7 | 8 | NA | NA | Frank Welker,Mindy Cohn,Grey Griffin,Matthew L... | Animation,Adventure,Comedy,Crime,Drama,Family,... | United States | ... | 23 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix | 9 |
| 4 | 788 | Sword Art Online | 2012 | 18 | 7.6 | NA | NA | Yoshitsugu Matsuoka,Haruka Tomatsu,Bryce Papen... | Animation,Action,Adventure,Comedy,Drama,Fantas... | Japan | ... | 24 | tv series | 4 | 1 | 1 | 0 | 0 | 1 | Netflix | 9 |
5 rows × 22 columns
fig = px.bar(y = netflix_genres_most_tvshows['Title'][:15],
x = netflix_genres_most_tvshows['Number of Genres'][:15],
color = netflix_genres_most_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Highest Number of Genres : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_genres_least_tvshows['Title'][:15],
x = netflix_genres_least_tvshows['Number of Genres'][:15],
color = netflix_genres_least_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Lowest Number of Genres : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Hulu']==1].reset_index()
hulu_genres_most_tvshows = hulu_genres_most_tvshows.drop(['index'], axis = 1)
hulu_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Hulu']==1].reset_index()
hulu_genres_least_tvshows = hulu_genres_least_tvshows.drop(['index'], axis = 1)
hulu_genres_most_tvshows.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2406 | Steven Universe | 2013 | 7 | 8.2 | 100 | NA | Zach Callison,Deedee Magno,Michaela Dietz,Este... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,South Korea,Spain,Japan,Mexico | ... | 11 | tv series | 6 | 0 | 1 | 0 | 0 | 1 | Hulu | 11 |
| 1 | 2275 | Gravity Falls | 2012 | 7 | 8.9 | 100 | NA | Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ... | Animation,Adventure,Comedy,Drama,Family,Fantas... | United States,Argentina,Australia,United Kingd... | ... | 23 | tv series | 2 | 0 | 1 | 0 | 1 | 1 | Disney+ | 10 |
| 2 | 2490 | Star vs. the Forces of Evil | 2015 | 7 | 8 | NA | NA | Eden Sher,Adam McArthur,Grey Griffin,Daron Nef... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,Spain,United Kingdom,Mexico,Japan | ... | 22 | tv series | 4 | 0 | 1 | 0 | 1 | 1 | Disney+ | 9 |
| 3 | 2867 | OK K.O.! Let's Be Heroes | 2017 | 7 | 7.1 | NA | NA | Courtenay Taylor,Ashly Burch,Ian Jones-Quartey... | Animation,Short,Action,Adventure,Comedy,Family... | United States,South Korea | ... | 11 | tv series | 3 | 0 | 1 | 0 | 0 | 1 | Hulu | 9 |
| 4 | 723 | Case Closed | 1996 | 7 | 8.4 | NA | NA | Minami Takayama,Wakana Yamazaki,Hoang Khuyet,A... | Animation,Action,Adventure,Comedy,Crime,Drama,... | Japan,Italy,United Kingdom,Mexico,Spain | ... | 25 | tv series | 53 | 1 | 1 | 0 | 0 | 1 | Netflix | 9 |
5 rows × 22 columns
fig = px.bar(y = hulu_genres_most_tvshows['Title'][:15],
x = hulu_genres_most_tvshows['Number of Genres'][:15],
color = hulu_genres_most_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Highest Number of Genres : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_genres_least_tvshows['Title'][:15],
x = hulu_genres_least_tvshows['Number of Genres'][:15],
color = hulu_genres_least_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Lowest Number of Genres : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Prime Video']==1].reset_index()
prime_video_genres_most_tvshows = prime_video_genres_most_tvshows.drop(['index'], axis = 1)
prime_video_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Prime Video']==1].reset_index()
prime_video_genres_least_tvshows = prime_video_genres_least_tvshows.drop(['index'], axis = 1)
prime_video_genres_most_tvshows.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3896 | Infinity Train | 2019 | 7 | 8.5 | 100 | NA | Ashley Johnson,Owen Dennis,Jeremy Crutchley,Ki... | Animation,Action,Adventure,Drama,Family,Fantas... | United States | ... | 11 | tv series | 4 | 0 | 0 | 1 | 0 | 1 | Prime Video | 10 |
| 1 | 3806 | Eerie, Indiana | 1991 | 7 | 8.2 | 100 | NA | Omri Katz,Justin Shenkarow,Mary-Margaret Humes... | Adventure,Comedy,Drama,Family,Fantasy,Horror,M... | United States | ... | 30 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video | 9 |
| 2 | 4027 | Bakugan Battle Brawlers | 2007 | 7 | 5.5 | NA | NA | Jason Deline,Julie Lemieux,Carter Hayden,Shawn... | Animation,Action,Adventure,Comedy,Drama,Family... | Canada,South Korea,Japan | ... | 22 | tv series | 2 | 0 | 0 | 1 | 0 | 1 | Prime Video | 9 |
| 3 | 4290 | GetBackers | 2002 | 7 | 7.4 | NA | NA | Darren Pleavin,Shanon Weaver,Jason Liebrecht,O... | Animation,Action,Adventure,Comedy,Crime,Drama,... | Japan,Italy,United Kingdom,Mexico,United State... | ... | 24 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video | 9 |
| 4 | 5081 | The Fish Guyz | 2017 | NR | 7.3 | NA | NA | Rob Paulsen,Jeff Bennett,Cam Clarke,Jim Cummin... | Animation,Action,Adventure,Comedy,Crime,Fantas... | NA | ... | NA | tv series | NA | 0 | 0 | 1 | 0 | 1 | Prime Video | 8 |
5 rows × 22 columns
fig = px.bar(y = prime_video_genres_most_tvshows['Title'][:15],
x = prime_video_genres_most_tvshows['Number of Genres'][:15],
color = prime_video_genres_most_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Highest Number of Genres : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_genres_least_tvshows['Title'][:15],
x = prime_video_genres_least_tvshows['Number of Genres'][:15],
color = prime_video_genres_least_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Lowest Number of Genres : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Disney+']==1].reset_index()
disney_genres_most_tvshows = disney_genres_most_tvshows.drop(['index'], axis = 1)
disney_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Disney+']==1].reset_index()
disney_genres_least_tvshows = disney_genres_least_tvshows.drop(['index'], axis = 1)
disney_genres_most_tvshows.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5314 | Gargoyles | 1994 | 7 | 8.1 | NA | NA | Keith David,Salli Richardson-Whitfield,Jeff Be... | Animation,Action,Adventure,Crime,Drama,Family,... | United States | ... | 30 | tv series | 2 | 0 | 0 | 0 | 1 | 1 | Disney+ | 10 |
| 1 | 2275 | Gravity Falls | 2012 | 7 | 8.9 | 100 | NA | Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ... | Animation,Adventure,Comedy,Drama,Family,Fantas... | United States,Argentina,Australia,United Kingd... | ... | 23 | tv series | 2 | 0 | 1 | 0 | 1 | 1 | Disney+ | 10 |
| 2 | 2490 | Star vs. the Forces of Evil | 2015 | 7 | 8 | NA | NA | Eden Sher,Adam McArthur,Grey Griffin,Daron Nef... | Animation,Action,Adventure,Comedy,Drama,Family... | United States,Spain,United Kingdom,Mexico,Japan | ... | 22 | tv series | 4 | 0 | 1 | 0 | 1 | 1 | Disney+ | 9 |
| 3 | 5321 | TaleSpin | 1990 | 0 | 7.6 | NA | NA | Ed Gilbert,Jim Cummings,Sally Struthers,R.J. W... | Animation,Action,Adventure,Comedy,Drama,Family... | United States | ... | 30 | tv series | 1 | 0 | 0 | 0 | 1 | 1 | Disney+ | 8 |
| 4 | 5373 | Randy Cunningham: 9th Grade Ninja | 2012 | 7 | 6.7 | NA | NA | Ben Schwartz,Andrew Lewis Caldwell,Facundo Rey... | Animation,Short,Action,Adventure,Comedy,Family... | United Kingdom,United States,Ireland | ... | 15 | tv series | 2 | 0 | 0 | 0 | 1 | 1 | Disney+ | 8 |
5 rows × 22 columns
fig = px.bar(y = disney_genres_most_tvshows['Title'][:15],
x = disney_genres_most_tvshows['Number of Genres'][:15],
color = disney_genres_most_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Highest Number of Genres : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_genres_least_tvshows['Title'][:15],
x = disney_genres_least_tvshows['Number of Genres'][:15],
color = disney_genres_least_tvshows['Number of Genres'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
title = 'TV Shows with Lowest Number of Genres : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The TV Show with Highest Number of Genres Ever Got is '{df_genres_most_tvshows['Title'][0]}' : '{df_genres_most_tvshows['Number of Genres'].max()}'\n
The TV Show with Lowest Number of Genres Ever Got is '{df_genres_least_tvshows['Title'][0]}' : '{df_genres_least_tvshows['Number of Genres'].min()}'\n
The TV Show with Highest Number of Genres on 'Netflix' is '{netflix_genres_most_tvshows['Title'][0]}' : '{netflix_genres_most_tvshows['Number of Genres'].max()}'\n
The TV Show with Lowest Number of Genres on 'Netflix' is '{netflix_genres_least_tvshows['Title'][0]}' : '{netflix_genres_least_tvshows['Number of Genres'].min()}'\n
The TV Show with Highest Number of Genres on 'Hulu' is '{hulu_genres_most_tvshows['Title'][0]}' : '{hulu_genres_most_tvshows['Number of Genres'].max()}'\n
The TV Show with Lowest Number of Genres on 'Hulu' is '{hulu_genres_least_tvshows['Title'][0]}' : '{hulu_genres_least_tvshows['Number of Genres'].min()}'\n
The TV Show with Highest Number of Genres on 'Prime Video' is '{prime_video_genres_most_tvshows['Title'][0]}' : '{prime_video_genres_most_tvshows['Number of Genres'].max()}'\n
The TV Show with Lowest Number of Genres on 'Prime Video' is '{prime_video_genres_least_tvshows['Title'][0]}' : '{prime_video_genres_least_tvshows['Number of Genres'].min()}'\n
The TV Show with Highest Number of Genres on 'Disney+' is '{disney_genres_most_tvshows['Title'][0]}' : '{disney_genres_most_tvshows['Number of Genres'].max()}'\n
The TV Show with Lowest Number of Genres on 'Disney+' is '{disney_genres_least_tvshows['Title'][0]}' : '{disney_genres_least_tvshows['Number of Genres'].min()}'\n
''')
The TV Show with Highest Number of Genres Ever Got is 'Steven Universe' : '11'
The TV Show with Lowest Number of Genres Ever Got is 'Love & Vets' : '1'
The TV Show with Highest Number of Genres on 'Netflix' is 'Spy Kids: Mission Critical' : '9'
The TV Show with Lowest Number of Genres on 'Netflix' is 'Louis Theroux: Miami Mega-Jail' : '1'
The TV Show with Highest Number of Genres on 'Hulu' is 'Steven Universe' : '11'
The TV Show with Lowest Number of Genres on 'Hulu' is 'Tengo Talento, Mucho Talento' : '1'
The TV Show with Highest Number of Genres on 'Prime Video' is 'Infinity Train' : '10'
The TV Show with Lowest Number of Genres on 'Prime Video' is 'Iconic Characters' : '1'
The TV Show with Highest Number of Genres on 'Disney+' is 'Gargoyles' : '10'
The TV Show with Lowest Number of Genres on 'Disney+' is 'Love & Vets' : '1'
print(f'''
Accross All Platforms the Average Number of Genres is '{round(df_tvshows_count_genres['Number of Genres'].mean(), ndigits = 2)}'\n
The Average Number of Genres on 'Netflix' is '{round(netflix_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
The Average Number of Genres on 'Hulu' is '{round(hulu_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
The Average Number of Genres on 'Prime Video' is '{round(prime_video_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
The Average Number of Genres on 'Disney+' is '{round(disney_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Number of Genres is '2.52'
The Average Number of Genres on 'Netflix' is '2.6'
The Average Number of Genres on 'Hulu' is '2.74'
The Average Number of Genres on 'Prime Video' is '2.29'
The Average Number of Genres on 'Disney+' is '3.74'
print(f'''
Accross All Platforms Total Count of Genre is '{df_tvshows_count_genres['Number of Genres'].max()}'\n
Total Count of Genre on 'Netflix' is '{netflix_genres_tvshows['Number of Genres'].max()}'\n
Total Count of Genre on 'Hulu' is '{hulu_genres_tvshows['Number of Genres'].max()}'\n
Total Count of Genre on 'Prime Video' is '{prime_video_genres_tvshows['Number of Genres'].max()}'\n
Total Count of Genre on 'Disney+' is '{disney_genres_tvshows['Number of Genres'].max()}'\n
''')
Accross All Platforms Total Count of Genre is '11'
Total Count of Genre on 'Netflix' is '9'
Total Count of Genre on 'Hulu' is '11'
Total Count of Genre on 'Prime Video' is '10'
Total Count of Genre on 'Disney+' is '10'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_count_genres['Number of Genres'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_count_genres['Number of Genres'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Genres s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_genres_tvshows['Number of Genres'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genres_tvshows['Number of Genres'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genres_tvshows['Number of Genres'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_genres_tvshows['Number of Genres'], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
df_lan = df_tvshows_genre['Genres'].str.split(',').apply(pd.Series).stack()
del df_tvshows_genre['Genres']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Genre'
df_tvshows_genre = df_tvshows_genre.join(df_lan)
df_tvshows_genre.drop_duplicates(inplace = True)
df_tvshows_genre.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Country | Language | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Genre | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | United States | English | ... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | Action |
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | United States | English | ... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | Drama |
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | United States | English | ... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | Sci-Fi |
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | United States | English | ... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | Thriller |
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | United States | English | ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix | Comedy |
5 rows × 21 columns
genre_count = df_tvshows_genre.groupby('Genre')['Title'].count()
genre_tvshows = df_tvshows_genre.groupby('Genre')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
genre_data_tvshows = pd.concat([genre_count, genre_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
genre_data_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_tvshows = netflix_genre_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
hulu_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_tvshows = hulu_genre_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
prime_video_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_tvshows = prime_video_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
disney_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_tvshows = disney_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
# Genre with TV Shows Counts - All Platforms Combined
genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 7 | Drama | 1852 | 754 | 552 | 650 | 41 |
| 4 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 2 | Animation | 985 | 341 | 372 | 312 | 75 |
| 6 | Documentary | 834 | 306 | 138 | 412 | 29 |
| 8 | Family | 729 | 238 | 195 | 263 | 110 |
| 0 | Action | 725 | 277 | 264 | 220 | 51 |
| 5 | Crime | 673 | 294 | 194 | 231 | 6 |
| 1 | Adventure | 670 | 246 | 217 | 215 | 78 |
| 9 | Fantasy | 631 | 241 | 246 | 188 | 42 |
| 23 | Thriller | 578 | 238 | 177 | 198 | 9 |
fig = px.bar(x = genre_data_tvshows['Genre'][:50],
y = genre_data_tvshows['TV Shows Count'][:50],
color = genre_data_tvshows['TV Shows Count'][:50],
color_continuous_scale = 'Teal_r',
labels = { 'x' : 'Genre', 'y' : 'TV Shows Count'},
title = 'Major Genres : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_genre_high_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_genre_high_tvshows = df_genre_high_tvshows.drop(['index'], axis = 1)
# filter = (genre_data_tvshows['TV Shows Count'] == (genre_data_tvshows['TV Shows Count'].max()))
# df_genre_high_tvshows = genre_data_tvshows[filter]
# highest_rated_tvshows = genre_data_tvshows.loc[genre_data_tvshows['TV Shows Count'].idxmax()]
print('\nGenre with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_genre_high_tvshows.head(5)
Genre with Highest Ever TV Shows Count are : All Platforms Combined
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Drama | 1852 | 754 | 552 | 650 | 41 |
| 1 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 2 | Animation | 985 | 341 | 372 | 312 | 75 |
| 3 | Documentary | 834 | 306 | 138 | 412 | 29 |
| 4 | Family | 729 | 238 | 195 | 263 | 110 |
fig = px.bar(y = df_genre_high_tvshows['Genre'][:15],
x = df_genre_high_tvshows['TV Shows Count'][:15],
color = df_genre_high_tvshows['TV Shows Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Highest TV Shows : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_genre_low_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_genre_low_tvshows = df_genre_low_tvshows.drop(['index'], axis = 1)
# filter = (genre_data_tvshows['TV Shows Count'] == (genre_data_tvshows['TV Shows Count'].min()))
# df_genre_low_tvshows = genre_data_tvshows[filter]
print('\nGenre with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_genre_low_tvshows.head(5)
Genre with Lowest Ever TV Shows Count are : All Platforms Combined
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Western | 47 | 9 | 11 | 31 | 1 |
| 1 | News | 53 | 10 | 28 | 18 | 0 |
| 2 | Musical | 75 | 31 | 14 | 19 | 18 |
| 3 | War | 94 | 38 | 17 | 42 | 0 |
| 4 | Talk-Show | 107 | 28 | 48 | 34 | 2 |
fig = px.bar(y = df_genre_low_tvshows['Genre'][:15],
x = df_genre_low_tvshows['TV Shows Count'][:15],
color = df_genre_low_tvshows['TV Shows Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Lowest TV Shows Count : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{genre_data_tvshows['Genre'].unique().shape[0]}' unique Genre Count s were Given, They were Like this,\n
{genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Genre'].unique()[:5]}\n
The Highest Ever TV Shows Count Ever Any TV Show Got is '{df_genre_high_tvshows['Genre'][0]}' : '{df_genre_high_tvshows['TV Shows Count'].max()}'\n
The Lowest Ever TV Shows Count Ever Any TV Show Got is '{df_genre_low_tvshows['Genre'][0]}' : '{df_genre_low_tvshows['TV Shows Count'].min()}'\n
''')
Total '26' unique Genre Count s were Given, They were Like this,
['Drama' 'Comedy' 'Animation' 'Documentary' 'Family']
The Highest Ever TV Shows Count Ever Any TV Show Got is 'Drama' : '1852'
The Lowest Ever TV Shows Count Ever Any TV Show Got is 'Western' : '47'
fig = px.pie(genre_data_tvshows[:10], names = 'Genre', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Genre')
fig.show()
# netflix_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_genre_tvshows = netflix_genre_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
netflix_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_high_tvshows = netflix_genre_high_tvshows.drop(['index'], axis = 1)
netflix_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_genre_low_tvshows = netflix_genre_low_tvshows.drop(['index'], axis = 1)
netflix_genre_high_tvshows.head(5)
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Drama | 1852 | 754 | 552 | 650 | 41 |
| 1 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 2 | Animation | 985 | 341 | 372 | 312 | 75 |
| 3 | Documentary | 834 | 306 | 138 | 412 | 29 |
| 4 | Crime | 673 | 294 | 194 | 231 | 6 |
fig = px.bar(x = netflix_genre_high_tvshows['Genre'][:15],
y = netflix_genre_high_tvshows['Netflix'][:15],
color = netflix_genre_high_tvshows['Netflix'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Highest TV Shows : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# hulu_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_genre_tvshows = hulu_genre_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
hulu_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_high_tvshows = hulu_genre_high_tvshows.drop(['index'], axis = 1)
hulu_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_genre_low_tvshows = hulu_genre_low_tvshows.drop(['index'], axis = 1)
hulu_genre_high_tvshows.head(5)
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 1 | Drama | 1852 | 754 | 552 | 650 | 41 |
| 2 | Animation | 985 | 341 | 372 | 312 | 75 |
| 3 | Action | 725 | 277 | 264 | 220 | 51 |
| 4 | Reality-TV | 571 | 167 | 247 | 186 | 22 |
fig = px.bar(x = hulu_genre_high_tvshows['Genre'][:15],
y = hulu_genre_high_tvshows['Hulu'][:15],
color = hulu_genre_high_tvshows['Hulu'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Highest TV Shows : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# prime_video_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_genre_tvshows = prime_video_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
prime_video_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_high_tvshows = prime_video_genre_high_tvshows.drop(['index'], axis = 1)
prime_video_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_genre_low_tvshows = prime_video_genre_low_tvshows.drop(['index'], axis = 1)
prime_video_genre_high_tvshows.head(5)
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Drama | 1852 | 754 | 552 | 650 | 41 |
| 1 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 2 | Documentary | 834 | 306 | 138 | 412 | 29 |
| 3 | Animation | 985 | 341 | 372 | 312 | 75 |
| 4 | Family | 729 | 238 | 195 | 263 | 110 |
fig = px.bar(x = prime_video_genre_high_tvshows['Genre'][:15],
y = prime_video_genre_high_tvshows['Prime Video'][:15],
color = prime_video_genre_high_tvshows['Prime Video'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Highest TV Shows : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# disney_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_genre_tvshows = disney_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
disney_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_high_tvshows = disney_genre_high_tvshows.drop(['index'], axis = 1)
disney_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_genre_low_tvshows = disney_genre_low_tvshows.drop(['index'], axis = 1)
disney_genre_high_tvshows.head(5)
| Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Family | 729 | 238 | 195 | 263 | 110 |
| 1 | Comedy | 1571 | 569 | 600 | 461 | 90 |
| 2 | Adventure | 670 | 246 | 217 | 215 | 78 |
| 3 | Animation | 985 | 341 | 372 | 312 | 75 |
| 4 | Action | 725 | 277 | 264 | 220 | 51 |
fig = px.bar(x = disney_genre_high_tvshows['Genre'][:15],
y = disney_genre_high_tvshows['Disney+'][:15],
color = disney_genre_high_tvshows['Disney+'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
title = 'Genre with Highest TV Shows : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(genre_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(genre_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre TV Shows Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(disney_genre_tvshows['Disney+'][:50], color = 'darkblue', legend = True, kde = True)
sns.histplot(prime_video_genre_tvshows['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genre_tvshows['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genre_tvshows['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()
print(f'''
The Genre with Highest TV Shows Count Ever Got is '{df_genre_high_tvshows['Genre'][0]}' : '{df_genre_high_tvshows['TV Shows Count'].max()}'\n
The Genre with Lowest TV Shows Count Ever Got is '{df_genre_low_tvshows['Genre'][0]}' : '{df_genre_low_tvshows['TV Shows Count'].min()}'\n
The Genre with Highest TV Shows Count on 'Netflix' is '{netflix_genre_high_tvshows['Genre'][0]}' : '{netflix_genre_high_tvshows['Netflix'].max()}'\n
The Genre with Lowest TV Shows Count on 'Netflix' is '{netflix_genre_low_tvshows['Genre'][0]}' : '{netflix_genre_low_tvshows['Netflix'].min()}'\n
The Genre with Highest TV Shows Count on 'Hulu' is '{hulu_genre_high_tvshows['Genre'][0]}' : '{hulu_genre_high_tvshows['Hulu'].max()}'\n
The Genre with Lowest TV Shows Count on 'Hulu' is '{hulu_genre_low_tvshows['Genre'][0]}' : '{hulu_genre_low_tvshows['Hulu'].min()}'\n
The Genre with Highest TV Shows Count on 'Prime Video' is '{prime_video_genre_high_tvshows['Genre'][0]}' : '{prime_video_genre_high_tvshows['Prime Video'].max()}'\n
The Genre with Lowest TV Shows Count on 'Prime Video' is '{prime_video_genre_low_tvshows['Genre'][0]}' : '{prime_video_genre_low_tvshows['Prime Video'].min()}'\n
The Genre with Highest TV Shows Count on 'Disney+' is '{disney_genre_high_tvshows['Genre'][0]}' : '{disney_genre_high_tvshows['Disney+'].max()}'\n
The Genre with Lowest TV Shows Count on 'Disney+' is '{disney_genre_low_tvshows['Genre'][0]}' : '{disney_genre_low_tvshows['Disney+'].min()}'\n
''')
The Genre with Highest TV Shows Count Ever Got is 'Drama' : '1852'
The Genre with Lowest TV Shows Count Ever Got is 'Western' : '47'
The Genre with Highest TV Shows Count on 'Netflix' is 'Drama' : '754'
The Genre with Lowest TV Shows Count on 'Netflix' is 'Western' : '9'
The Genre with Highest TV Shows Count on 'Hulu' is 'Comedy' : '600'
The Genre with Lowest TV Shows Count on 'Hulu' is 'Western' : '11'
The Genre with Highest TV Shows Count on 'Prime Video' is 'Drama' : '650'
The Genre with Lowest TV Shows Count on 'Prime Video' is 'News' : '18'
The Genre with Highest TV Shows Count on 'Disney+' is 'Family' : '110'
The Genre with Lowest TV Shows Count on 'Disney+' is 'War' : '0'
# Distribution of tvshows genre in each platform
plt.figure(figsize = (20, 5))
plt.title('Genre with TV Shows Count for All Platforms')
sns.violinplot(x = genre_data_tvshows['TV Shows Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()
# Distribution of Genre TV Shows Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_genre_tvshows['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_genre_tvshows['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_genre_tvshows['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_genre_tvshows['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()
print(f'''
Accross All Platforms the Average TV Shows Count of Genre is '{round(genre_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Genre on 'Netflix' is '{round(netflix_genre_tvshows['Netflix'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Genre on 'Hulu' is '{round(hulu_genre_tvshows['Hulu'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Genre on 'Prime Video' is '{round(prime_video_genre_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Genre on 'Disney+' is '{round(disney_genre_tvshows['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average TV Shows Count of Genre is '495.88'
The Average TV Shows Count of Genre on 'Netflix' is '182.96'
The Average TV Shows Count of Genre on 'Hulu' is '161.42'
The Average TV Shows Count of Genre on 'Prime Video' is '170.92'
The Average TV Shows Count of Genre on 'Disney+' is '29.3'
print(f'''
Accross All Platforms Total Count of Genre is '{genre_data_tvshows['Genre'].unique().shape[0]}'\n
Total Count of Genre on 'Netflix' is '{netflix_genre_tvshows['Genre'].unique().shape[0]}'\n
Total Count of Genre on 'Hulu' is '{hulu_genre_tvshows['Genre'].unique().shape[0]}'\n
Total Count of Genre on 'Prime Video' is '{prime_video_genre_tvshows['Genre'].unique().shape[0]}'\n
Total Count of Genre on 'Disney+' is '{disney_genre_tvshows['Genre'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Genre is '26'
Total Count of Genre on 'Netflix' is '26'
Total Count of Genre on 'Hulu' is '26'
Total Count of Genre on 'Prime Video' is '26'
Total Count of Genre on 'Disney+' is '23'
plt.figure(figsize = (20, 5))
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Netflix'][:10], color = 'red')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Disney+'][:10], color = 'darkblue')
plt.xlabel('Genre', fontsize = 20)
plt.ylabel('TV Shows Count', fontsize = 20)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_g_ax1 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_g_ax2 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_g_ax3 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_g_ax4 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_g_ax1 = sns.barplot(y = netflix_genre_tvshows['Genre'][:10], x = netflix_genre_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = hulu_genre_tvshows['Genre'][:10], x = hulu_genre_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = prime_video_genre_tvshows['Genre'][:10], x = prime_video_genre_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = disney_genre_tvshows['Genre'][:10], x = disney_genre_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre TV Shows Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_genre_tvshows['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_genre_tvshows['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_genre_tvshows['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_genre_tvshows['Disney+'][:10], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_g_ax1 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
plt.show()
df_tvshows_mixed_genres.drop(df_tvshows_mixed_genres.loc[df_tvshows_mixed_genres['Genres'] == "NA"].index, inplace = True)
# df_tvshows_mixed_genres = df_tvshows_mixed_genres[df_tvshows_mixed_genres.Genre != "NA"]
df_tvshows_mixed_genres.drop(df_tvshows_mixed_genres.loc[df_tvshows_mixed_genres['Number of Genres'] == 1].index, inplace = True)
df_tvshows_mixed_genres.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Genres | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | 4 |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix | 5 |
| 5 | 6 | Suburra | 2015 | NR | 7.9 | 91 | NA | Alessandro Borghi,Giacomo Ferrara,Filippo Nigr... | Action,Crime | Italy | ... | 50 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix | 2 |
| 6 | 7 | A Wednesday! | 2008 | NR | 8.1 | NA | Neeraj Pandey | NA | Comedy,Family,Fantasy,Horror | United States | ... | 104 | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix | 4 |
| 7 | 8 | Retribution | 2015 | NR | 7 | 28 | NA | Joanna Vanderham,John Lynch,Joe Dempsie,Julie ... | Drama,Mystery,Thriller | United Kingdom | ... | 231 | tv series | 1 | 1 | 0 | 1 | 0 | 1 | Netflix | 3 |
5 rows × 22 columns
mixed_genres_count = df_tvshows_mixed_genres.groupby('Genres')['Title'].count()
mixed_genres_tvshows = df_tvshows_mixed_genres.groupby('Genres')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_genres_data_tvshows = pd.concat([mixed_genres_count, mixed_genres_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count', 'Genres' : 'Mixed Genre'})
mixed_genres_data_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
mixed_genres_data_tvshows.head(5)
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 629 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 745 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
| 912 | Drama,Romance | 76 | 38 | 18 | 26 | 0 |
| 739 | Crime,Drama,Mystery | 67 | 19 | 21 | 33 | 0 |
| 796 | Documentary,Crime | 66 | 32 | 24 | 20 | 0 |
# Mixed Genre with TV Shows Counts - All Platforms Combined
mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 629 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 745 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
| 912 | Drama,Romance | 76 | 38 | 18 | 26 | 0 |
| 739 | Crime,Drama,Mystery | 67 | 19 | 21 | 33 | 0 |
| 796 | Documentary,Crime | 66 | 32 | 24 | 20 | 0 |
| 530 | Animation,Family | 66 | 19 | 10 | 40 | 1 |
| 720 | Crime,Drama | 60 | 29 | 13 | 22 | 0 |
| 749 | Crime,Drama,Thriller | 54 | 27 | 8 | 20 | 0 |
| 663 | Comedy,Drama,Romance | 51 | 25 | 17 | 13 | 0 |
| 815 | Documentary,History | 48 | 14 | 3 | 28 | 3 |
df_mixed_genres_high_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.drop(['index'], axis = 1)
# filter = (mixed_genres_data_tvshows['TV Shows Count'] = = (mixed_genres_data_tvshows['TV Shows Count'].max()))
# df_mixed_genres_high_tvshows = mixed_genres_data_tvshows[filter]
# highest_rated_tvshows = mixed_genres_data_tvshows.loc[mixed_genres_data_tvshows['TV Shows Count'].idxmax()]
print('\nMixed Genre with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_genres_high_tvshows.head(5)
Mixed Genre with Highest Ever TV Shows Count are : All Platforms Combined
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 1 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
| 2 | Drama,Romance | 76 | 38 | 18 | 26 | 0 |
| 3 | Crime,Drama,Mystery | 67 | 19 | 21 | 33 | 0 |
| 4 | Documentary,Crime | 66 | 32 | 24 | 20 | 0 |
fig = px.bar(y = df_mixed_genres_high_tvshows['Mixed Genre'][:15],
x = df_mixed_genres_high_tvshows['TV Shows Count'][:15],
color = df_mixed_genres_high_tvshows['TV Shows Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Genre'},
title = 'TV Shows with Highest Number of Mixed Genres : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_mixed_genres_low_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_mixed_genres_low_tvshows = df_mixed_genres_low_tvshows.drop(['index'], axis = 1)
# filter = (mixed_genres_data_tvshows['TV Shows Count'] = = (mixed_genres_data_tvshows['TV Shows Count'].min()))
# df_mixed_genres_low_tvshows = mixed_genres_data_tvshows[filter]
print('\nMixed Genre with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_genres_low_tvshows.head(5)
Mixed Genre with Lowest Ever TV Shows Count are : All Platforms Combined
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Short,Comedy,Drama,Fantasy,Romance | 1 | 0 | 0 | 1 | 0 |
| 1 | Adventure,Biography,Drama,History,Romance | 1 | 0 | 0 | 1 | 0 |
| 2 | Adventure,Comedy,Crime,Drama,Romance,Thriller | 1 | 1 | 0 | 0 | 0 |
| 3 | Adventure,Comedy,Drama,Family | 1 | 0 | 0 | 1 | 0 |
| 4 | Adventure,Comedy,Drama,Family,Fantasy,Horror,M... | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_mixed_genres_low_tvshows['Mixed Genre'][:15],
x = df_mixed_genres_low_tvshows['TV Shows Count'][:15],
color = df_mixed_genres_low_tvshows['TV Shows Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Genre'},
title = 'TV Shows with Lowest Number of Mixed Genres : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_tvshows_genres['Genres'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see TV Shows from Total '{mixed_genres_data_tvshows['Mixed Genre'].unique().shape[0]}' Mixed Genre, They were Like this, \n
{mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Mixed Genre'].head(5).unique()} etc. \n
The Mixed Genre with Highest TV Shows Count have '{mixed_genres_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_mixed_genres_high_tvshows['Mixed Genre'][0]}', &\n
The Mixed Genre with Lowest TV Shows Count have '{mixed_genres_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_mixed_genres_low_tvshows['Mixed Genre'][0]}'
''')
Total '5109' Titles are available on All Platforms, out of which
You Can Choose to see TV Shows from Total '1006' Mixed Genre, They were Like this,
['Comedy,Drama' 'Crime,Drama,Mystery,Thriller' 'Drama,Romance'
'Crime,Drama,Mystery' 'Documentary,Crime'] etc.
The Mixed Genre with Highest TV Shows Count have '92' TV Shows Available is 'Comedy,Drama', &
The Mixed Genre with Lowest TV Shows Count have '1' TV Shows Available is 'Short,Comedy,Drama,Fantasy,Romance'
fig = px.pie(mixed_genres_data_tvshows[:10], names = 'Mixed Genre', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Mixed Genre')
fig.show()
# netflix_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_genres_tvshows = netflix_mixed_genres_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
netflix_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_high_tvshows = netflix_mixed_genres_high_tvshows.drop(['index'], axis = 1)
netflix_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_genres_low_tvshows = netflix_mixed_genres_low_tvshows.drop(['index'], axis = 1)
netflix_mixed_genres_high_tvshows.head(5)
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 1 | Drama,Romance | 76 | 38 | 18 | 26 | 0 |
| 2 | Documentary,Crime | 66 | 32 | 24 | 20 | 0 |
| 3 | Crime,Drama | 60 | 29 | 13 | 22 | 0 |
| 4 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
# hulu_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_genres_tvshows = hulu_mixed_genres_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
hulu_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_high_tvshows = hulu_mixed_genres_high_tvshows.drop(['index'], axis = 1)
hulu_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_genres_low_tvshows = hulu_mixed_genres_low_tvshows.drop(['index'], axis = 1)
hulu_mixed_genres_high_tvshows.head(5)
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 1 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
| 2 | Documentary,Crime | 66 | 32 | 24 | 20 | 0 |
| 3 | Game-Show,Reality-TV | 30 | 6 | 23 | 7 | 0 |
| 4 | Animation,Comedy | 40 | 7 | 23 | 11 | 2 |
# prime_video_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_genres_tvshows = prime_video_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
prime_video_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_high_tvshows = prime_video_mixed_genres_high_tvshows.drop(['index'], axis = 1)
prime_video_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_genres_low_tvshows = prime_video_mixed_genres_low_tvshows.drop(['index'], axis = 1)
prime_video_mixed_genres_high_tvshows.head(5)
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Animation,Family | 66 | 19 | 10 | 40 | 1 |
| 1 | Crime,Drama,Mystery | 67 | 19 | 21 | 33 | 0 |
| 2 | Crime,Drama,Mystery,Thriller | 79 | 27 | 27 | 32 | 1 |
| 3 | Comedy,Drama | 92 | 44 | 28 | 29 | 0 |
| 4 | Documentary,History | 48 | 14 | 3 | 28 | 3 |
# disney_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_genres_tvshows = disney_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
disney_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_high_tvshows = disney_mixed_genres_high_tvshows.drop(['index'], axis = 1)
disney_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_genres_low_tvshows = disney_mixed_genres_low_tvshows.drop(['index'], axis = 1)
disney_mixed_genres_high_tvshows.head(5)
| Mixed Genre | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Comedy,Family | 48 | 12 | 16 | 18 | 9 |
| 1 | Adventure,Drama,Family | 9 | 1 | 0 | 1 | 7 |
| 2 | Comedy,Drama,Family | 13 | 2 | 6 | 0 | 6 |
| 3 | Animation,Adventure,Comedy,Family | 13 | 5 | 2 | 3 | 4 |
| 4 | Animation,Action,Adventure,Comedy,Sci-Fi | 6 | 1 | 1 | 0 | 4 |
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_genres_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_genres_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_tvshows = netflix_mixed_genres_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
hulu_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_tvshows = hulu_mixed_genres_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
prime_video_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_tvshows = prime_video_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
disney_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_tvshows = disney_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre TV Shows Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_mixed_genres_tvshows['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_genres_tvshows['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_genres_tvshows['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_genres_tvshows['Disney+'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
print(f'''
The Mixed Genre with Highest TV Shows Count Ever Got is '{df_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{df_mixed_genres_high_tvshows['TV Shows Count'].max()}'\n
The Mixed Genre with Lowest TV Shows Count Ever Got is '{df_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{df_mixed_genres_low_tvshows['TV Shows Count'].min()}'\n
The Mixed Genre with Highest TV Shows Count on 'Netflix' is '{netflix_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{netflix_mixed_genres_high_tvshows['Netflix'].max()}'\n
The Mixed Genre with Lowest TV Shows Count on 'Netflix' is '{netflix_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{netflix_mixed_genres_low_tvshows['Netflix'].min()}'\n
The Mixed Genre with Highest TV Shows Count on 'Hulu' is '{hulu_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{hulu_mixed_genres_high_tvshows['Hulu'].max()}'\n
The Mixed Genre with Lowest TV Shows Count on 'Hulu' is '{hulu_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{hulu_mixed_genres_low_tvshows['Hulu'].min()}'\n
The Mixed Genre with Highest TV Shows Count on 'Prime Video' is '{prime_video_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{prime_video_mixed_genres_high_tvshows['Prime Video'].max()}'\n
The Mixed Genre with Lowest TV Shows Count on 'Prime Video' is '{prime_video_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{prime_video_mixed_genres_low_tvshows['Prime Video'].min()}'\n
The Mixed Genre with Highest TV Shows Count on 'Disney+' is '{disney_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{disney_mixed_genres_high_tvshows['Disney+'].max()}'\n
The Mixed Genre with Lowest TV Shows Count on 'Disney+' is '{disney_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{disney_mixed_genres_low_tvshows['Disney+'].min()}'\n
''')
The Mixed Genre with Highest TV Shows Count Ever Got is 'Comedy,Drama' : '92'
The Mixed Genre with Lowest TV Shows Count Ever Got is 'Short,Comedy,Drama,Fantasy,Romance' : '1'
The Mixed Genre with Highest TV Shows Count on 'Netflix' is 'Comedy,Drama' : '44'
The Mixed Genre with Lowest TV Shows Count on 'Netflix' is 'Action,Adventure,Fantasy,Mystery,Romance,Sci-Fi' : '0'
The Mixed Genre with Highest TV Shows Count on 'Hulu' is 'Comedy,Drama' : '28'
The Mixed Genre with Lowest TV Shows Count on 'Hulu' is 'Sport,Talk-Show' : '0'
The Mixed Genre with Highest TV Shows Count on 'Prime Video' is 'Animation,Family' : '40'
The Mixed Genre with Lowest TV Shows Count on 'Prime Video' is 'Sport,Talk-Show' : '0'
The Mixed Genre with Highest TV Shows Count on 'Disney+' is 'Comedy,Family' : '9'
The Mixed Genre with Lowest TV Shows Count on 'Disney+' is 'Comedy,Drama' : '0'
print(f'''
Accross All Platforms the Average TV Shows Count of Mixed Genre is '{round(mixed_genres_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Mixed Genre on 'Netflix' is '{round(netflix_mixed_genres_tvshows['Netflix'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Mixed Genre on 'Hulu' is '{round(hulu_mixed_genres_tvshows['Hulu'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Mixed Genre on 'Prime Video' is '{round(prime_video_mixed_genres_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
The Average TV Shows Count of Mixed Genre on 'Disney+' is '{round(disney_mixed_genres_tvshows['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average TV Shows Count of Mixed Genre is '3.21'
The Average TV Shows Count of Mixed Genre on 'Netflix' is '2.35'
The Average TV Shows Count of Mixed Genre on 'Hulu' is '2.19'
The Average TV Shows Count of Mixed Genre on 'Prime Video' is '2.29'
The Average TV Shows Count of Mixed Genre on 'Disney+' is '1.45'
print(f'''
Accross All Platforms Total Count of Mixed Genre is '{mixed_genres_data_tvshows['Mixed Genre'].unique().shape[0]}'\n
Total Count of Mixed Genre on 'Netflix' is '{netflix_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
Total Count of Mixed Genre on 'Hulu' is '{hulu_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
Total Count of Mixed Genre on 'Prime Video' is '{prime_video_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
Total Count of Mixed Genre on 'Disney+' is '{disney_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Mixed Genre is '1006'
Total Count of Mixed Genre on 'Netflix' is '513'
Total Count of Mixed Genre on 'Hulu' is '477'
Total Count of Mixed Genre on 'Prime Video' is '489'
Total Count of Mixed Genre on 'Disney+' is '100'
plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Genre', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_g_ax1 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_mg_ax1 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_mg_ax2 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_mg_ax3 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_mg_ax4 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre TV Shows Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_genres_tvshows['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_genres_tvshows['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_genres_tvshows['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_genres_tvshows['Disney+'][:50], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_mg_ax1 = sns.barplot(y = netflix_mixed_genres_tvshows['Mixed Genre'][:10], x = netflix_mixed_genres_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_mg_ax2 = sns.barplot(y = hulu_mixed_genres_tvshows['Mixed Genre'][:10], x = hulu_mixed_genres_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_mg_ax3 = sns.barplot(y = prime_video_mixed_genres_tvshows['Mixed Genre'][:10], x = prime_video_mixed_genres_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_mg_ax4 = sns.barplot(y = disney_mixed_genres_tvshows['Mixed Genre'][:10], x = disney_mixed_genres_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
plt.show()
fig = go.Figure(go.Funnel(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['TV Shows Count'][:10]))
fig.show()
genres = df_tvshows_genre_all['Genres'].str.get_dummies(',')
df_tvshows_genre_all = pd.concat([df_tvshows_genre_all, genres], axis = 1, sort = False)
data_investigate(df_tvshows_genre_all)
No of Rows : 5109
No of Coloums : 47
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider', 'Action', 'Adventure', 'Animation', 'Biography',
'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy',
'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'News',
'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show',
'Thriller', 'War', 'Western'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Seasons object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
Action int64
Adventure int64
Animation int64
Biography int64
Comedy int64
Crime int64
Documentary int64
Drama int64
Family int64
Fantasy int64
Game-Show int64
History int64
Horror int64
Music int64
Musical int64
Mystery int64
News int64
Reality-TV int64
Romance int64
Sci-Fi int64
Short int64
Sport int64
Talk-Show int64
Thriller int64
War int64
Western int64
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Seasons 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
Action 0.0
Adventure 0.0
Animation 0.0
Biography 0.0
Comedy 0.0
Crime 0.0
Documentary 0.0
Drama 0.0
Family 0.0
Fantasy 0.0
Game-Show 0.0
History 0.0
Horror 0.0
Music 0.0
Musical 0.0
Mystery 0.0
News 0.0
Reality-TV 0.0
Romance 0.0
Sci-Fi 0.0
Short 0.0
Sport 0.0
Talk-Show 0.0
Thriller 0.0
War 0.0
Western 0.0
dtype: float64
**************************************************
Pictorial Representation :
#Select the features on the basis of ehich you want to cluster
features = df_tvshows_genre_all[['Action', 'Adventure', 'Animation', 'Biography',
'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy',
'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'News',
'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show',
'Thriller', 'War', 'Western']].astype(int)
#Scaling the data
scaler = StandardScaler()
scaled_data = scaler.fit_transform(features)
#Using TSNE
tsne = TSNE(n_components = 2)
transformed_genre = tsne.fit_transform(scaled_data)
#KMeans - Elbow Method
distortions = []
K = range(1, 100)
for k in K:
kmean = KMeans(n_clusters = k)
kmean.fit(scaled_data)
distortions.append(kmean.inertia_)
fig = px.line(x = K, y = distortions, title = 'The Elbow Method Showing The Optimal K',
labels = {'x':'No of Clusters', 'y':'Distortions'})
fig.show()
#Kmeans
cluster = KMeans(n_clusters = 27)
group_pred = cluster.fit_predict(scaled_data)
tsne_df = pd.DataFrame(np.column_stack((transformed_genre, group_pred, df_tvshows_genre_all['Title'], df_tvshows_genre_all['Genres'], df_tvshows_genre_all['Service Provider'])), columns = ['X', 'Y', 'Group', 'Title', 'Genres', 'Service Provider'])
fig = px.scatter(tsne_df, x = 'X', y = 'Y', hover_data = ['Title', 'Genres', 'Service Provider'], color = 'Group',
color_discrete_sequence = px.colors.cyclical.IceFire)
fig.show()